School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
Summary
I am Abdur Rehman, a dedicated and accomplished professional who completed a Ph.D. in the School of Computer Science at NCBA&E in Lahore, Pakistan. My academic journey has been guided by a passion for computer science and a drive to make meaningful contributions to the field. Currently serving as an Associate Professor at NCBA&E, I have gained valuable experience in shaping the minds of future professionals while fostering an environment of academic excellence. In tandem with my academic role, I have also had the privilege of working as a Game Developer at GameObject in Lahore, Pakistan, accumulating more than 10 years of hands-on experience in the dynamic realm of game development.
My research endeavors have been particularly focused on cutting-edge areas such as Smart City technologies, Healthcare, Machine Learning, Blockchain, Federated learning, and Network Security. Over the span of my career, I have produced a substantial body of work, comprising 15+ research articles published in reputable journals with notable impact factors, reflecting a cumulative impact factor of 70+. This research has enabled me to contribute to the advancement of knowledge and to address challenges that are central to our ever-evolving technological landscape.
Neural computing, as an influential factor of artificial intelligence, is an industry that has managed to achieve an extensive array of innovations. This paper presents an overview of the recent advancements in the field of neural computing, which are focused on state-of-the-art architectures, novel computational paradigms, and their applications in intelligent systems. The paper traces the development of neural networks, from the original artificial neural network (ANN) through deep learning models and on to neuromorphic computing. In other words, the main points of emphasis are breakthroughs in hardware acceleration, hybrid models, and bio-inspired computing, which are responsible for inte... More >
The global healthcare systems have faced unprecedented challenges due to the COVID-19 pandemic, necessitating innovative neural computing solutions to inform critical decision-making. In this study, we introduce a neural-inspired machine learning framework to predict COVID-19 mortality risk, utilizing a dataset comprising over one million records. We developed and evaluated a suite of advanced models—Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Gradient Boost Classifier, and a neural ensemble-based Voting Classifier—to analyze the influence of demographics, symptoms, and preexisting conditions on mortality predictions. Through meticulous feature engineering... More >
Graphical Abstract
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